In front-end approaches, objects detected in new images are compared with previous images.
If matches between new and old images are found, then corresponding objects are associated to the same unique identifier.
These matches are typically reliable as the disparity between two consecutive images are usually small.
When the robot come back to a previously visited place after traversing a long distance, costly global optimization must be performed to achieve global loop closures.
These data associations by front-end procedures are taken as reliable and true, and then passed to a SLAM solver [1]}, [2]}.
SLAM++ [1]} is one such front-end approach.
Full 3D scans of chairs and tables are created and used as templates. When new point clouds are observed during testing, they are matched to pre-built templates.
Successfully matched detections are often of high credibility. A SLAM solver is then run on these reliable detections to optimize object locations and camera poses.
In semantic SLAM [2]}, Civera et al. created a library of six objects, used SURF features to detect these objects, and then ran an EKF to simultaneously localize robot and map objects.
